A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and...A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.展开更多
利用环境星HJ-CCD影像与同步获取的LAI实测数据生成江苏省江淮之间西部和里下河地区水稻的30 m HJ/LAI,对MODIS/LAI数据产品和利用MODIS数据与4尺度几何光学模型反演的LAI数据集进行质量评价,结果表明,不同植被指数与研究区水稻LAI的相...利用环境星HJ-CCD影像与同步获取的LAI实测数据生成江苏省江淮之间西部和里下河地区水稻的30 m HJ/LAI,对MODIS/LAI数据产品和利用MODIS数据与4尺度几何光学模型反演的LAI数据集进行质量评价,结果表明,不同植被指数与研究区水稻LAI的相关性差别很大,其中GNDVI与水稻LAI的相关性最好,R2为0.72,估算精度达70.89%,而RMSE仅为1.38,适于该区水稻LAI的遥感估算;研究区MODIS/LAI和基于4尺度几何光学模型反演的LAI与HJ/LAI的变化趋势较为一致,均呈现出西南和东北部LAI值较低、北部和中部LAI值较高的特征,但MODIS/LAI和基于4尺度几何光学模型反演的LAI不仅变化范围较小,而且偏低明显,MODIS/LAI的低估现象更为严重;在1 km尺度上,MODIS/LAI和基于4尺度几何光学模型反演的LAI的精度分别为60.21%和66.56%,与HJ/LAI比较的R2分别为0.09和0.28(N=2 585),在0.01水平上显著相关。展开更多
Accumulated temperature,which is now widely used in agronomy,is an important ecological factor to the growth of plants,but few relative studies have been found on the vegetation area of floodplain grasslands in Poyang...Accumulated temperature,which is now widely used in agronomy,is an important ecological factor to the growth of plants,but few relative studies have been found on the vegetation area of floodplain grasslands in Poyang Lake.This research used the classification and regression tree(CART)to classify normalized vegetation area index derived from MODIS LAI(Moderate Resolution Imaging Spectroradiometer Leaf Area Index)images from 2008 to 2014,according to different climate indexes,such as mean daily air temperature(n),accumulated temperature(jw),daily maximum temperature(g),daily minimum temperature(d),accumulative precipitation(j),water level(s)and average water level for 20 days preceding(a).The results showed that:(1)The accumulated temperature and the 20-day average WL(water level)were found to have the greatest impact on variation in wetland vegetation area,and they both dominated the classification process twice;(2)Two classification thresholds of accumulated temperature were 790°C and 1784°C,approximately corresponding to the beginning of April and midMay;(3)790°C could also be used as a threshold to select remote sensing images to analysis the annual variability of vegetation,i.e.while accumulated temperature is lower than 790°C,remote sensing images of similar accumulated temperature rather than similar date should be selected from different years for comparison.We also found that,effects of different hydrological factors on area of floodplain grasslands showed stage characteristics:(1)From January to March,water level changes slowly with less rainfall,as a result,the 20-day average WL which can interpret the hydrologic characteristics smoothly showed significant importance in this stage;(2)While entering April,intense rainfall make accumulative precipitation to be the dominating factor of classification;(3)From late April to mid-May,in condition of accumulative precipitation higher than 405 mm,daily water level is of most importance,because to the flood recession process as well as rapid water level fluctuations.展开更多
基金This work was supported by the China Postdoctoral Science Foundation(No.20060390326)the key international S&T cooperation project of China(No.2004DFA06300).
文摘A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer （MODIS） leaf area index （LAI） time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control （QC） of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China.
文摘利用环境星HJ-CCD影像与同步获取的LAI实测数据生成江苏省江淮之间西部和里下河地区水稻的30 m HJ/LAI,对MODIS/LAI数据产品和利用MODIS数据与4尺度几何光学模型反演的LAI数据集进行质量评价,结果表明,不同植被指数与研究区水稻LAI的相关性差别很大,其中GNDVI与水稻LAI的相关性最好,R2为0.72,估算精度达70.89%,而RMSE仅为1.38,适于该区水稻LAI的遥感估算;研究区MODIS/LAI和基于4尺度几何光学模型反演的LAI与HJ/LAI的变化趋势较为一致,均呈现出西南和东北部LAI值较低、北部和中部LAI值较高的特征,但MODIS/LAI和基于4尺度几何光学模型反演的LAI不仅变化范围较小,而且偏低明显,MODIS/LAI的低估现象更为严重;在1 km尺度上,MODIS/LAI和基于4尺度几何光学模型反演的LAI的精度分别为60.21%和66.56%,与HJ/LAI比较的R2分别为0.09和0.28(N=2 585),在0.01水平上显著相关。
基金This work was supported by the National Natural Science Foundation of China“Study on the dynamic mechanism of grassland ecosystem response to climate change in Qinghai Plateau”under grant number U20A2098the Second Tibet Plateau Scientific Expedition and Research Program(STEP)under grant number 2019QZKK0804.
文摘Accumulated temperature,which is now widely used in agronomy,is an important ecological factor to the growth of plants,but few relative studies have been found on the vegetation area of floodplain grasslands in Poyang Lake.This research used the classification and regression tree(CART)to classify normalized vegetation area index derived from MODIS LAI(Moderate Resolution Imaging Spectroradiometer Leaf Area Index)images from 2008 to 2014,according to different climate indexes,such as mean daily air temperature(n),accumulated temperature(jw),daily maximum temperature(g),daily minimum temperature(d),accumulative precipitation(j),water level(s)and average water level for 20 days preceding(a).The results showed that:(1)The accumulated temperature and the 20-day average WL(water level)were found to have the greatest impact on variation in wetland vegetation area,and they both dominated the classification process twice;(2)Two classification thresholds of accumulated temperature were 790°C and 1784°C,approximately corresponding to the beginning of April and midMay;(3)790°C could also be used as a threshold to select remote sensing images to analysis the annual variability of vegetation,i.e.while accumulated temperature is lower than 790°C,remote sensing images of similar accumulated temperature rather than similar date should be selected from different years for comparison.We also found that,effects of different hydrological factors on area of floodplain grasslands showed stage characteristics:(1)From January to March,water level changes slowly with less rainfall,as a result,the 20-day average WL which can interpret the hydrologic characteristics smoothly showed significant importance in this stage;(2)While entering April,intense rainfall make accumulative precipitation to be the dominating factor of classification;(3)From late April to mid-May,in condition of accumulative precipitation higher than 405 mm,daily water level is of most importance,because to the flood recession process as well as rapid water level fluctuations.
